When choosing where to submit your neural network research, it helps to compare NCA against other journals tracked on the LetPub SCI Indexing Database: Journal Name Key Strengths / Focus LetPub User Sentiment
从LetPub分区的"计算机科学大类学科"方向看，NCA在软件工程领域细分方向排名，在人工智能方向排名 第59位（共450种，87%百分位） 。 neural computing and applications letpub
从实际录用内容来看，期刊强调。 When choosing where to submit your neural network
Neural Computing and Applications (NCA) is a hybrid, peer-reviewed international journal with a rich history dating back to 1993. Published by Springer London, it has established itself as a key publication venue for research at the intersection of neural computing and real-world problem-solving. The journal is a quarterly publication, though some sources indicate a monthly frequency in recent years, reflecting its growth. in Artificial Intelligence and Software by major ranking
in Artificial Intelligence and Software by major ranking bodies. Submissions
In modern smart manufacturing environments, the accurate and real-time detection of surface defects remains a critical challenge due to the scarcity of defective samples and the high variability of defect scales. Traditional Convolutional Neural Networks (CNNs) often struggle to extract meaningful features from small or subtle defects in complex industrial backgrounds. This paper proposes a novel hybrid deep learning framework, named the , to address these limitations. The proposed architecture integrates a pre-trained ResNet-50 backbone with a custom-designed Multi-Scale Feature Fusion (MSFF) module and a Convolutional Block Attention Module (CBAM). The MSFF module captures hierarchical contextual information at different resolutions, while the CBAM highlights salient defect regions while suppressing background noise. We evaluated the proposed method on three publicly available benchmark datasets: NEU-DET (steel surfaces), PCB-DAT (printed circuit boards), and MT-DEF (magnetic tile defects). Experimental results demonstrate that AGMS-Net achieves a mean Average Precision (mAP) of 89.4% on the NEU-DET dataset, outperforming state-of-the-art methods such as YOLOv5 and Faster R-CNN by a margin of 3.2% and 4.1%, respectively. Furthermore, the model maintains a competitive inference speed, making it suitable for real-time industrial deployment.